Reinforcement learning based control of batch polymerisation processes

Control of batch polymerization has been a challenging task. In this work, we have tried to use Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) based control on addressing the existing challenges. RL is a class of machine learning wherein an agent directly interacts with the environment and learns from its experience. The RL consist of an agent who takes an action, and the action changes the state of the environment. Based on old and new state agent gets a reward, which is reinforcement for its future actions. In this work, we have implemented RL and DRL based control for batch polymerization of Polymethyl methacrylate (PMMA). In both the controllers, the input variable considered was jacket temperature, while the reactor temperature was the output variable. Both the controllers have been found to achieve the given setpoint, while the DRL controller being faster than the RL controllers. Further, RL and DRL control with risk sensitivity were also carried out to accommodate the process constraints.